A machine learning approach for predicting the nucleophilicity of organic molecules

Abstract

Nucleophilicity provides an important information about chemical reactivity of organic molecules. Experimental determination of nucleophilicity parameter is tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that...

A machine learning approach for predicting the empirical polarity of organic solvents

“…Random forest model outperformed every other model screened with train and test R 2 values of 0.975 and 0.927, respectively. In fact, RF is recognized as one of the best tree-based ML models and has found plethora of application in chemical sciences for predicting yields, 38 solubilities, 22 nucleophilicities 25 etc. The most important feature that sets RF apart from the other tree-based algorithms is that it uses bootstrap sampling technique that reduces variance and bias, thus increasing precision.…”

“…Several quantitative structure activity relationship studies and ML methods have proven quantum chemical and thermodynamic descriptors to be important tools for predicting various chemical and physical properties of molecules. [23][24][25][26]30 For our predictive modelling application, density functional theory (DFT) based computational approaches were used as they enable the generation of various electronic and thermodynamic descriptors in the most efficient and reliable manner. 31,32 Since the solvents employed in our study belong to 13 diverse categories, choosing appropriate descriptors that accurately represents the wholesome properties of these molecules was a big challenge.…”

“…ML methods have also found application in chemical sciences and have emerged as powerful tools to predict various chemical and physical properties of organic molecules, particularly when the dataset is large and complex. [16][17][18][19] For example, ML models have been successfully employed to predict pK a , 20 toxicity, 21 solubility, 22,23 electrophilicity, 24 nucleophilicity, 25,26 and reactivity [27][28][29] of organic molecules that provides deep insight into their reactivity pattern, drug-likeliness, and physicochemical characteristics, which can assist a chemist in a faster and more accurate drug design. Considering the vast amount of experimental data available on the empirical polarity of organic liquids, we sought out to develop a ML model to accurately predict the E T (30) values by developing a novel dataset of computationally derived topological and molecular descriptors.…”

The computational road to reactivity scales

“…In recent years, the data set size has been expanded to a number of reference structures greater than 750 to learn correlations between different descriptors and reactivity parameters, which enable highly accurate predictions of reactivity. Saini et al 59 report their best result using a NN for 752 structures, while Tavakoli et al 60 use methyl anion/cation affinities in solution (MAA* and MCA*, see below) as inputs for over 2421 structures to train a graph attention network. A graph neural network was trained by Nie et al 61 on nearly 900 nucleophiles from Mayr's database, in which electronic and, additionally, solvent descriptors were employed.…”

A machine learning approach for predicting the fluorination strength of electrophilic fluorinating reagents

“…Recently, machine learning (ML) methods have emerged as an attractive tool for predicting various chemical and physical properties of organic molecules, such as polarity, 20 solubility, 21,22 pK a , 23 electrophilicity, 24 nucleophilicity, 25,26 bond dissociation energies, 27,28 etc. In fact, the scientific community has started to view them as a potential alternative to expensive quantum chemical methods, as they can provide similar accuracy at a much lower computational cost.…”